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AI Evaluation Should Work With Humans

06:00 · August 17, 2026 · arXiv cs.AI RSS

AI Evaluation Should Work With Humans

This position paper argues that the dominant paradigm of AI evaluation (which focuses on superhuman autonomous performance and so implicitly targets the goal of replacing humans) is guiding AI development in the wrong direction. Instead, the AI community should pivot to evaluating the performance of human--AI teams. We argue that this collaborative shift will foster AI systems that act as true complements to human capabilities and therefore lead to far better societal outcomes than will the current process.

Summary

The dominant approach to AI evaluation measures systems against the benchmark of autonomous, superhuman performance on narrowly defined tasks. This framing, the authors contend, steers development toward the implicit objective of replacing human labor rather than supporting it. By rewarding models that operate independently and outperform people, current metrics risk producing tools that are poorly aligned with the ways humans actually work and make decisions.

The position paper therefore calls for a shift in evaluation practice toward joint human–AI performance. Instead of isolating model capabilities, assessments would examine how effectively teams of people and systems solve problems together. Such metrics would reward designs that fill gaps in human expertise, reduce cognitive load, or improve decision quality under realistic constraints.

The authors argue that this collaborative orientation would encourage the creation of AI systems that function as genuine complements to human abilities. The resulting technologies, they maintain, are more likely to deliver positive societal outcomes than systems optimized solely for standalone superiority.

Why it matters

This paper aligns strongly with the Dutch and EU focus on ethical, human-centric AI and human oversight. It provides researchers with a conceptual foundation to develop new evaluation frameworks that prioritize human-AI collaboration over autonomous replacement, which is highly actionable for Dutch AI policy and enterprise deployment.

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ai-alignmentevaluation-benchmarkshuman-ai-interactionhuman-oversight-frameworksjoint performancellm-benchmarks
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06:00 · August 18, 2026

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This article is highly relevant for Dutch AI researchers and practitioners focused on ethical AI, aligning strongly with the Netherlands' and EU's emphasis on transparent and trustworthy AI systems. It provides a critical framework for advancing LLM evaluation beyond simple value alignment toward robust normative reasoning.

Relevance 85 · Audience 95

Safety, or Just Capability? A Validity Audit of Agent-Safety Benchmarks

06:00 · August 3, 2026

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Relevance 82 · Audience 88

Constructive Alignment: Governing Preference Dynamics in Human-AI Interaction

06:00 · July 2, 2026

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This research aligns perfectly with the Dutch AI market's strong strategic focus on ethical, transparent, and human-centric AI. It provides advanced researchers with a rigorous, control-theoretic framework to address the long-term societal impacts and potential manipulative risks of adaptive AI systems.

Relevance 85 · Audience 95

ASI-Bench: At the Dawn of Artificial Superintelligence

06:00 · August 19, 2026

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Relevance 62 · Audience 88

Diagnostic Foundation for Evaluating LLMs' Research Integrity as Co-Scientists

06:00 · August 15, 2026

Diagnostic Foundation for Evaluating LLMs' Research Integrity as Co-Scientists

This research is highly relevant for Dutch AI researchers and institutions focused on ethical AI deployment. It provides a concrete framework to evaluate and mitigate research misconduct risks when integrating LLMs into scientific workflows, aligning perfectly with the EU's emphasis on trustworthy AI.

Relevance 85 · Audience 95

InfraBench: Evaluating Infrastructure Agents Across Layers, Lifecycle, and Risk

06:00 · August 13, 2026

InfraBench: Evaluating Infrastructure Agents Across Layers, Lifecycle, and Risk

This research is highly relevant for Dutch AI researchers and MLOps practitioners developing autonomous agents, as it provides a rigorous, open-source framework for testing agent reliability and safety. Its focus on risk assessment and operational side-effects aligns strongly with the Netherlands' strategic emphasis on transparent, ethical, and secure AI deployments.

Relevance 85 · Audience 95

AgentStream: How Well Do Self-Evolving LLM Agents Perform Under Streaming Tasks?

06:00 · August 4, 2026

AgentStream: How Well Do Self-Evolving LLM Agents Perform Under Streaming Tasks?

This research is highly relevant for Dutch AI researchers developing autonomous LLM agents, providing a rigorous framework for evaluating continuous learning in realistic deployment scenarios. Understanding how model capabilities gate self-evolution is crucial for building robust and reliable AI systems.

Relevance 85 · Audience 95

CoT-Core: Accelerating LLM Evaluation via CoT-Aware Coreset Selection

06:00 · August 4, 2026

CoT-Core: Accelerating LLM Evaluation via CoT-Aware Coreset Selection

Directly actionable for Dutch AI researchers and advanced practitioners developing or continuously evaluating LLMs: reduces compute overhead while preserving ranking fidelity, aligns with EU emphasis on efficient and transparent AI, and requires no historical logs.

Relevance 72 · Audience 88